电厂汽轮机高中压转子振动突变故障识别研究

为了保证电厂汽轮机能够在高温、高转速环境中安全稳定运行，提出电厂汽轮机高中压转子振动突变故障识别方法。根据电厂汽轮机高中压转子振动突变故障时的轴系振动特点，利用一体化电涡流位移传感器采集相应的故障信号，利用小波包分析提取高中压转子振动突变故障特征，将提取到的故障特征作为输入量，输入采用人工鱼群算法优化的RBF神经网络中，输出电厂汽轮机高中压转子振动突变故障类型识别结果。在实验过程中采用本文方法对质量不平衡、转子热弯曲、转轴不对中、转动部件飞脱、动静碰磨、汽流激振、结构共振、结构刚度不足、转子裂纹等9种常见故障进行识别。实验结果表明，该方法分解并重构的电厂汽轮机高中压转子振动突变故障信号质量较高，获得故障识别结果与实际故障相同，识别精度高，结果具备可靠性。

Study on Sudden Vibration Fault Identification of High and Intermediate Pressure Rotor of Steam Turbine in Power Plant

In order to ensure the safe and stable operation of the power plant steam turbine in the high temperature and high speed environment, a sudden vibration fault identification method of high and intermediate pressure rotor of steam turbine in power plant is proposed. According to the shafting vibration characteristics of the high and medium pressure rotor vibration of the power plant turbine, the integrated eddy current displacement sensor is used to collect the corresponding fault signal, and the wavelet packet analysis is used to extract the high and medium pressure rotor vibration mutation fault characteristics, and the extracted fault characteristics are used as input. Input the fault features into the RBF neural network optimized by artificial fish swarm algorithm, and output the identification result of the vibration mutation type of the high and medium pressure rotor of the steam turbine of the power plant. During the experiment, the method in this paper was used to identify 9 common faults, such as mass unbalance, rotor thermal bending, shaft misalignment, rotating parts flying off, dynamic and static friction, steam flow excitation, structural resonance, insufficient structural rigidity, and rotor cracks. The experimental results show that the method decomposes and reconstructs the high-pressure rotor vibration mutation fault signal of the power plant steam turbine with high quality, and the obtained fault identification result is the same as the actual fault, the identification accuracy is high, and the result is reliable.